Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data
With the rapid advancement of technology in Malaysia, the number of cybercrimes is also increasing. To stop the increase in cybercrimes, everyone, including normal citizens, needs to know how secure they are while using digital appliances. A system is developed to predict the risk of users based on...
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| Format: | Article |
| Language: | English English |
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The Science and Information (SAI) Organization Limited
2023
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| Online Access: | https://eprints.ums.edu.my/id/eprint/37849/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/37849/2/FULL%20TEXT.pdf |
| _version_ | 1825715431446413312 |
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| author | Ting, Tin Tin Khiew, Jie Xin Ali Aitizaz Lee, Kuok Tiung Teoh, Chong Keat Hasan Sarwar |
| author_facet | Ting, Tin Tin Khiew, Jie Xin Ali Aitizaz Lee, Kuok Tiung Teoh, Chong Keat Hasan Sarwar |
| author_sort | Ting, Tin Tin |
| collection | UMS |
| description | With the rapid advancement of technology in Malaysia, the number of cybercrimes is also increasing. To stop the increase in cybercrimes, everyone, including normal citizens, needs to know how secure they are while using digital appliances. A system is developed to predict the risk of users based on their behaviour when they are online using real-life behavioural data obtained from a private university’s 207 undergraduates. Five supervised machine learning methods are being tested which are: Regression Logistics, K-Nearest Neighbour (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Naïve Bayesian Classifier with the aid of a tool, RapidMiner. The algorithms are used to construct, test, and validate three categories of cybercrime threat (Malware, Social Engineering, and Password Attack) predictive models. It was found that KNN model produces the highest accuracy and lowest classification error for all three categories of cybercrime threat. This system is believed to be crucial in alerting users with details of whether the consumer behaviour risk is high or low and what further actions can be taken to increase awareness. This system aims to prevent the rise in cybercrimes by providing a prediction of their risk levels in cybersecurity to encourage them to be more proactive in cybersecurity. |
| first_indexed | 2024-03-06T03:26:45Z |
| format | Article |
| id | ums.eprints-37849 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:26:45Z |
| publishDate | 2023 |
| publisher | The Science and Information (SAI) Organization Limited |
| record_format | dspace |
| spelling | ums.eprints-378492023-12-15T08:04:46Z https://eprints.ums.edu.my/id/eprint/37849/ Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data Ting, Tin Tin Khiew, Jie Xin Ali Aitizaz Lee, Kuok Tiung Teoh, Chong Keat Hasan Sarwar HV7431 Prevention of crime, methods, etc. QA75.5-76.95 Electronic computers. Computer science With the rapid advancement of technology in Malaysia, the number of cybercrimes is also increasing. To stop the increase in cybercrimes, everyone, including normal citizens, needs to know how secure they are while using digital appliances. A system is developed to predict the risk of users based on their behaviour when they are online using real-life behavioural data obtained from a private university’s 207 undergraduates. Five supervised machine learning methods are being tested which are: Regression Logistics, K-Nearest Neighbour (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Naïve Bayesian Classifier with the aid of a tool, RapidMiner. The algorithms are used to construct, test, and validate three categories of cybercrime threat (Malware, Social Engineering, and Password Attack) predictive models. It was found that KNN model produces the highest accuracy and lowest classification error for all three categories of cybercrime threat. This system is believed to be crucial in alerting users with details of whether the consumer behaviour risk is high or low and what further actions can be taken to increase awareness. This system aims to prevent the rise in cybercrimes by providing a prediction of their risk levels in cybersecurity to encourage them to be more proactive in cybersecurity. The Science and Information (SAI) Organization Limited 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/37849/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/37849/2/FULL%20TEXT.pdf Ting, Tin Tin and Khiew, Jie Xin and Ali Aitizaz and Lee, Kuok Tiung and Teoh, Chong Keat and Hasan Sarwar (2023) Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data. (IJACSA) International Journal of Advanced Computer Science and Applications, 14. pp. 832-840. ISSN 2158-107X https://dx.doi.org/10.14569/IJACSA.2023.0140987 |
| spellingShingle | HV7431 Prevention of crime, methods, etc. QA75.5-76.95 Electronic computers. Computer science Ting, Tin Tin Khiew, Jie Xin Ali Aitizaz Lee, Kuok Tiung Teoh, Chong Keat Hasan Sarwar Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data |
| title | Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data |
| title_full | Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data |
| title_fullStr | Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data |
| title_full_unstemmed | Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data |
| title_short | Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data |
| title_sort | machine learning based predictive modelling of cybersecurity threats utilising behavioural data |
| topic | HV7431 Prevention of crime, methods, etc. QA75.5-76.95 Electronic computers. Computer science |
| url | https://eprints.ums.edu.my/id/eprint/37849/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/37849/2/FULL%20TEXT.pdf |
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